Article written by Emil Eifrem for

Fraud has become a major issue for the insurance industry, with the Association of British Insurers (ABI) estimating that deceptive claims are responsible for adding an extra £50 to the premium of each customer’s annual bills.

The problem: this is hard to fight let alone spot in the first place. Insurance fraud attracts sophisticated criminal rings who are often very effective in circumventing fraud detection measures. In a typical hard fraud scenario, rings of fraudsters work together to stage fake accidents and claim soft tissue injuries. These fake accidents never happen, but are mere paper collisions complete with fake drivers, fake passengers, fake pedestrians and even fake witnesses.

Because soft tissue injuries are easy to falsify, difficult to validate, and expensive to treat, they are a favorite among fraudsters, who have even developed a term for them, “Cash for Crash”. Such rings normally include a number of roles. Fake collisions typically involve participation from professionals across several categories: doctors, who diagnose false injuries, lawyers, who file fraudulent claims, and bodyshops, which misrepresent damage to cars. Participants in the (false) accident will normally include drivers, passengers, pedestrians and witnesses.

How Graph Databases Can Help

Social network analytics tends not to be a strength of relational databases, the traditional business data system used in most insurance firms today. Discovering the ring requires a number of tables in a complex schema such as alleged/imaginary Accidents, Vehicles, Owners, Drivers, Passengers, Pedestrians, Witnesses, Providers, and joining these together multiple times — once per potential role — in order to uncover the full picture. Because such operations are so complex and costly, in computer performance, particularly for large data sets, this crucial form of analysis is often overlooked.

On the other hand, finding fraud rings with a graph database is a much simpler question (in technical terms) of following connections. Because graph databases are designed to query intricate connected networks, they can be used to identify fraud rings in a fairly straightforward fashion.

As a result, graph database queries can, and should, be added to the insurance company’s standard checks, at appropriate points in time— such as when the claim is filed—to flag suspected fraud rings in real time.